industry-research by rkreddyp/investrecipes
npx skills add https://github.com/rkreddyp/investrecipes --skill industry-research一项全面的技能,用于在包括消费、科技、医疗保健和金融行业在内的多个领域进行深入的行业研究。
此技能提供可重复使用的方法论、框架和最佳实践,用于分析行业趋势、识别关键市场参与者、理解市场动态、跟踪行业新闻以及预测未来前景。
行业特定新闻来源
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
市场数据来源
公司信息来源
浏览器自动化工作流
截图分析
趋势识别
市场分析
洞察生成
所有研究输出遵循此目录结构:
outputs/
└── <agent_name>/
└── <customer_name>/
├── reports/ # 最终 Markdown 研究报告
├── scripts/ # 生成的研究代码
├── raw/ # JSON/CSV 数据文件
└── screenshots/ # 来源的 PNG 截图
## [行业] 研究报告
**生成时间:** [日期/时间]
**研究周期:** [日期范围]
**分析的来源:** [来源列表]
---
### 执行摘要
[2-3 段关键发现概述]
---
### 行业趋势
[当前趋势分析及来源]
---
### 关键公司与市场领导者
[顶级公司及其市场地位分析]
---
### 市场动态
[市场规模、增长率、关键指标及来源]
---
### 近期发展
[行业新闻与事件,含日期和来源]
---
### 未来展望
[新兴趋势、预测和机遇]
---
### 来源归属
[使用的所有来源和 URL 列表]
import asyncio
from playwright.async_api import async_playwright
async def research_industry_trends(industry_sources):
"""
Research industry trends from multiple sources.
Args:
industry_sources (list): List of URLs to research
Returns:
dict: Research findings organized by source
"""
findings = {}
async with async_playwright() as p:
browser = await p.chromium.launch()
page = await browser.new_page(viewport={"width": 1920, "height": 1080})
for source_url in industry_sources:
try:
# Navigate and capture
await page.goto(source_url, wait_until="domcontentloaded", timeout=120000)
await page.screenshot(path=f"screenshot_{source_url.replace('/', '_')}.png", full_page=True)
# Extract data (implement based on page structure)
# ... extraction logic ...
findings[source_url] = extracted_data
except Exception as e:
print(f"Error researching {source_url}: {e}")
await browser.close()
return findings
async def extract_market_metrics(page, selector_mapping):
"""
Extract market metrics from a financial data page.
Args:
page: Playwright page object
selector_mapping (dict): Mapping of metric names to CSS selectors
Returns:
dict: Extracted metrics
"""
metrics = {}
for metric_name, selector in selector_mapping.items():
try:
element = await page.query_selector(selector)
if element:
text = await element.inner_text()
metrics[metric_name] = text.strip()
except Exception as e:
print(f"Error extracting {metric_name}: {e}")
return metrics
此技能设计为由专门的行业研究智能体使用:
consumer_researcher.md - 消费/零售行业tech_researcher.md - 科技行业healthcare_researcher.md - 医疗保健/生物技术行业finance_researcher.md - 金融/银行业每个智能体将此技能的方法论应用于其特定的行业领域。
每周安装数
163
代码仓库
GitHub 星标数
1
首次出现
Jan 23, 2026
安全审计
安装于
opencode154
codex145
gemini-cli144
github-copilot141
cursor141
kimi-cli136
A comprehensive skill for conducting deep industry research across multiple sectors including consumer, technology, healthcare, and finance industries.
This skill provides reusable methodologies, frameworks, and best practices for analyzing industry trends, identifying key market players, understanding market dynamics, tracking industry news, and forecasting future outlooks.
Industry-Specific News Sources
Market Data Sources
Company Information Sources
Browser Automation Workflow
Screenshot Analysis
Trend Identification
Market Analysis
Insight Generation
All research outputs follow this directory structure:
outputs/
└── <agent_name>/
└── <customer_name>/
├── reports/ # Final markdown research reports
├── scripts/ # Generated research code
├── raw/ # JSON/CSV data files
└── screenshots/ # PNG screenshots of sources
## [Industry] Research Report
**Generated:** [Date/Time]
**Research Period:** [Date range]
**Sources Analyzed:** [List of sources]
---
### Executive Summary
[2-3 paragraph overview of key findings]
---
### Industry Trends
[Current trends with analysis and sources]
---
### Key Companies & Market Leaders
[Top companies with market position and analysis]
---
### Market Dynamics
[Market size, growth rates, key metrics with sources]
---
### Recent Developments
[Industry news and events with dates and sources]
---
### Future Outlook
[Emerging trends, predictions, and opportunities]
---
### Source Attribution
[List of all sources and URLs used]
import asyncio
from playwright.async_api import async_playwright
async def research_industry_trends(industry_sources):
"""
Research industry trends from multiple sources.
Args:
industry_sources (list): List of URLs to research
Returns:
dict: Research findings organized by source
"""
findings = {}
async with async_playwright() as p:
browser = await p.chromium.launch()
page = await browser.new_page(viewport={"width": 1920, "height": 1080})
for source_url in industry_sources:
try:
# Navigate and capture
await page.goto(source_url, wait_until="domcontentloaded", timeout=120000)
await page.screenshot(path=f"screenshot_{source_url.replace('/', '_')}.png", full_page=True)
# Extract data (implement based on page structure)
# ... extraction logic ...
findings[source_url] = extracted_data
except Exception as e:
print(f"Error researching {source_url}: {e}")
await browser.close()
return findings
async def extract_market_metrics(page, selector_mapping):
"""
Extract market metrics from a financial data page.
Args:
page: Playwright page object
selector_mapping (dict): Mapping of metric names to CSS selectors
Returns:
dict: Extracted metrics
"""
metrics = {}
for metric_name, selector in selector_mapping.items():
try:
element = await page.query_selector(selector)
if element:
text = await element.inner_text()
metrics[metric_name] = text.strip()
except Exception as e:
print(f"Error extracting {metric_name}: {e}")
return metrics
This skill is designed to be used by specialized industry researcher agents:
consumer_researcher.md - Consumer/retail industrytech_researcher.md - Technology industryhealthcare_researcher.md - Healthcare/biotech industryfinance_researcher.md - Finance/banking industryEach agent applies this skill's methodologies to their specific industry domain.
Weekly Installs
163
Repository
GitHub Stars
1
First Seen
Jan 23, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
opencode154
codex145
gemini-cli144
github-copilot141
cursor141
kimi-cli136
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43,800 周安装